PROJECT SUMMARY/ABSTRACT
Suicide is the second leading cause of adolescent mortality in the US. Rates of adolescent suicide have
increased in recent years, along with the advent and growth of social media use (SMU) in this population. SMU
has been linked with sleep disruption, depressed mood, and suicidal ideation (SI), all of which are precursors of
suicidal behavior. Existing research on the impact of SMU on sleep, mood, and suicidality is limited by cross-
sectional designs and self-reported measures of SMU and sleep. To advance the field, prospective designs are
needed that include objective measures and intensive monitoring of high-risk samples to rigorously examine the
temporal relationships between SMU and suicide risk, defined as depressed mood and SI. This K01 proposal
uses precisely such a design to test a conceptual model in which SMU predicts sleep disruption, which, in turn,
contributes to depressed mood and SI among adolescents. The proposed study will harness smartphone
technology to assess
SMU using
real-time passive data capture, actigraphy, and ecological momentary
assessment (EMA) in high-risk adolescents. These methods, combined with in-person study visits, will examine
the temporal and unique within-person associations between SMU and sleep disruption (Aim 1), SMU and
suicide risk (Aim 2), and test whether sleep disruption mediates the relationship between SMU and suicide risk
(Aim 3). Data-driven approaches of supervised machine learning will leverage the high-dimensional, intensively-
monitored data to identify key SMU features predictive of suicide risk in adolescents (Exploratory Aim). The
candidate will build on her strong foundation in adolescent depression and sleep research by acquiring new
conceptual and methodological training in: 1) adolescent suicide risk, 2) technology and mental health, including
social media use and mobile technology for real-time assessment; and 3) advanced computational skills. The
candidate has assembled an interdisciplinary mentorship team to achieve her training goals within the
exceptional environment of the Department of Psychology at Rutgers University. Mentors (Selby, Kleiman, Brent,
and Moreno) are experts in adolescent suicide, intensive monitoring, and social media, and have had success
in mentoring early career scientists. The candidate’s consultants have extensive expertise in adolescent
development and EMA (Silk), adolescent sleep and actigraphy (Franzen), smartphone sensing (Ferreira) applied
to clinical health (Low), and advanced statistical and machine learning approaches (Wallace). The proposed
training and research will inform future R01 studies that use these innovative methods to identify and modify
clinically-actionable risk factors, including SMU and sleep disruption, to attenuate near-term risk for adolescent
suicide. This program of research has the potential to yield high-impact results that inform the development of
suicide prevention programs that are personalized, scalable, and delivered in real time. The proposed research
along with this training plan will uniquely position the candidate to be an independent investigator and leading
scholar in the important public health problem of adolescent suicide.